Graph theory analysis, as a mathematical tool, has been widely employed in studying the
connectivity of the brain to explore the structural organization. Through the computation of …

Automatic seizure detection has significant value in epilepsy diagnosis and treatment.
Although a variety of deep learning models have been proposed to automatically learn …

Wearable devices for seizure monitoring detection could significantly improve the quality of
life of epileptic patients. However, existing solutions that mostly rely on full electrode set of …

Deep learning (DL) is emerging as a successful technique for automatic detection and
differentiation of spontaneous seizures that may otherwise be missed or misclassified …

Epilepsy is a complex neurological disorder marked by recurrent seizures, often stemming
from abnormal discharge of the brain. Electroencephalogram (EEG) captures temporal and …

Increasing attention is being given to machine learning methods designed to aid clinicians
in treatment selection. Therefore, this has aroused a heightened focus on the auto-detect …

The power consumption data from residential households collected by smart meters exhibit
a diverse pattern temporally and among themselves. It is challenging to distinguish between …

The task of uncovering causal relationships among multivariate time series data stands as
an essential and challenging objective that cuts across a broad array of disciplines ranging …

The rapid development of wearable biomedical systems now enables real-time monitoring
of electroencephalography (EEG) signals. Acquisition of these signals relies on electrodes …

Neonatal seizure is a common neurological emergency in the neonatal intensive care unit
(NICU). Automatic neonatal epilepsy detection technology is of great significance to …